India’s healthcare future profoundly depends on effective healthcare data India generates and utilizes. Experts recently highlighted that cleaning, curating, and applying this data are paramount for progress. During a panel discussion titled “India’s Healthcare Odyssey: Navigating the Future with AI and Next-Gen Tech,” participants unanimously agreed on this crucial point. Prof. Anurag Agrawal, Head of the Koita Center for Digital Health, moderated the discussion. This article explores key insights from the experts.
Addressing the Healthcare Data India Paradox
Prof. Agrawal initiated the conversation by noting technology’s unpredictable nature. He compared discussing next-gen tech to rowing a boat, where one faces backward while moving forward. Although India might not yet be data-rich for its vast size, its future will be shaped by making existing data usable. Sudeep Dey, CIO & CISO at Aster DM Healthcare, underscored the plentiful data. However, he emphasized that clinical datasets need extensive cleansing and harmonization before effective use. Business data, conversely, is typically far more curated. Building a clinical data lake and generating useful insights for physicians can take significantly longer, often around 18 months, as Dey experienced.
Furthermore, healthcare information often resides in isolated systems. This necessitates robust collaboration with original equipment manufacturers and technology partners. Dey explained that even solving just three use cases required 18 months of effort. He stressed that clear goals are vital for effective data sharing. We must first define the problems we aim to solve. The data universe is immense; therefore, without specific use cases, it becomes overwhelming. Use cases simplify sharing, provided data is anonymized and stakeholders are appropriately involved.
Digital Enablement and Longitudinal Records
Prasad Krishnamorthy, Head of IHX Pvt Ltd, highlighted the immense scale of India’s data challenge. If a leading institute takes 18 months to curate its data, imagine the difficulties for the remaining 90% of providers. He detailed the numbers, noting over 45,000 providers, more than 100,000 pathology labs, and numerous clinics. The Ayushman Bharat Digital Mission (ABDM) envisions structuring, labeling, and analyzing all this data. Only then can India deliver preventive care and achieve universal health coverage.
Krishnamorthy shared a personal anecdote about his mother’s care journey. Even in urban tertiary hospitals, he had to carry bundles of physical files. Specialists often overlooked critical details, which he only caught due to his presence. A proper digital health exchange would prevent such errors. At IHX, 40% of cashless claims already flow through their exchange. This provides valuable hospitalization information, even without complete clinical data. The next essential step is digitizing unstructured clinical data. Moreover, creating longitudinal health records becomes possible. Krishnamorthy concluded that digital enablement across providers, built on common infrastructure, is a crucial first step. It requires joint efforts from technology companies and healthcare providers alike.
AI’s Role in Healthcare Data India’s Future
Dr. Tavpritesh Sethi, Head of the Center of Excellence in Healthcare at IIIT Delhi, used a compelling metaphor for AI in healthcare. He posited that if healthcare is a futuristic car, data serves as the fuel. Foundation models function as the engine. Significantly, we still require effective steering and navigation. Longitudinal and multimodal data, indeed, acts as the fuel. This includes integrating lab results, treatment charts, and ICU signals. Foundation models, therefore, become the powerful engine. Steering involves human oversight to ensure clinical safety. Navigation refers to emerging agentic AI systems that sequence tasks and execute them responsibly. We already possess the necessary components. The key, Sethi asserted, will be integrating them into practical use cases, citing his team’s ICU decision-making models.
Regarding messy or unannotated data, Sethi noted that modern AI architectures, such as contrastive learning, reduce the need for costly manual labeling. At AIIMS, his team explores how image-text pairings in histopathology can substitute manual annotation. He also mentioned global successes. For instance, Graph Neural Networks at MIT discovered antibiotics like Helicin (2020) and Abaucin (2023) using public datasets. With strong collaboration across technology, biology, and healthcare, India can achieve similar groundbreaking results. Ultimately, experts agreed that India’s healthcare future hinges not merely on generating more data, but on its responsible curation, sharing, and application.
Fostering Collaboration for Collective Wisdom
Prof. Agrawal effectively summarized the discussion into three critical stages. Firstly, moving from dirty to clean data is essential. Secondly, addressing the CIO’s harmonization challenge and binding data to patients creates longitudinal personal health records, aligning with ABDM. Thirdly, next-gen systems leverage foundation models to move beyond operational efficiency. These systems drive discovery, prediction, and innovation. He highlighted breakthroughs like TXGNN’s AI-powered drug repurposing for untreatable diseases. Additionally, AI-enabled ECG prediction models outperform traditional risk scores. AI-driven molecule synthesis at startups like LylaAI demonstrates emergent capacities of foundation models.
However, a significant challenge remains: the reluctance to share data. Everyone often considers their data “gold,” despite most breakthroughs stemming from public datasets. Dey underlined the importance of use-case-driven curation. Krishnamorthy emphasized digital enablement across providers. Sethi highlighted AI architectures that unlock value even from messy datasets. Prof. Agrawal concluded that while many systems abroad boast clean, longitudinal health records, India’s task is harder yet more exciting. Our odyssey involves transforming fragmented information into collective wisdom. This endeavor will demand collaboration across technology, medicine, policy, and patients alike.
Frequently Asked Questions
Q1: What is the primary challenge India faces regarding healthcare data?
India’s main challenge is not just generating vast amounts of healthcare data, but effectively cleaning, curating, ensuring interoperability, and intelligently using that data to derive actionable insights and improve patient care.
Q2: How does the Ayushman Bharat Digital Mission (ABDM) relate to India’s data challenge?
The ABDM vision aims to structure, label, and analyze all healthcare data from various providers, labs, and clinics. This will enable the delivery of preventive care and universal health coverage by creating a robust digital health ecosystem. The mission also seeks to provide every citizen with a unique digital health ID and access to a continuous Electronic Health Record (EHR).
Q3: How can Artificial Intelligence (AI) contribute to India’s healthcare data future?
AI can act as the “engine” for healthcare, using longitudinal and multimodal data as “fuel.” Foundation models and advanced AI architectures can process messy datasets, facilitate discovery, improve diagnostic accuracy, and drive innovation in treatment, even enabling new drug discoveries.
References
- India’s healthcare future hinges on data: Experts call for curation, sharing,and AI adoption – ETHealthworld
- India’s healthcare AI adoption faces hurdles in data, trust, equity, and workforce readiness – IMAWS
- Economic Survey: Adoption of AI in Indian healthcare sector faces several challenges
- Ayushman Bharat Digital Mission and India’s Health Landscape – P4i
- The Ayushman Bharat Digital Mission (ABDM): making of India’s Digital Health Story – PMC
- Under ABDM, health data exchange permitted only after patient consent – Medical Buyer
- Ayushman Bharat Digital Mission marks a Transformative Three-Year Journey towards enabling Digital Health
- AI in healthcare: trends and challenges in India | International Bar Association
- AI in healthcare improving patient outcomes, can offer curated treatments | Mint
- India’s evolving digital health strategy – PMC – PubMed Central
- Digital Health in India – Nishith Desai Associates
- From Data to Diagnosis Transforming Healthcare through Digitalization – PIB
- Catalyzing digital health in India – Arthur D. Little
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